Semisupervised Approach to Surrogate-Assisted Multiobjective Kernel Intuitionistic Fuzzy Clustering Algorithm for Color Image Segmentation

Abstract

Multiobjective evolutionary algorithms (MOEAs) are effective optimization methods. To improve the segmentation performance and time efficiency of MOEAs-based fuzzy clustering algorithms for color images, a semisupervised surrogate-assisted multiobjective kernel intuitionistic fuzzy clustering (S <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> MKIFC) algorithm is proposed in this article. The main contributions of S <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> MKIFC can be summarized as follows: 1) semisupervised kernel intuitionistic fuzzy objective functions are constructed for optimization to search satisfactory segmentation results; 2) to reduce the computational cost, the Kriging model is used to predict the values of objective functions instead of directly calculating the expensive objective functions; 3) a semisupervised selection strategy and a semisupervised model management mechanism are proposed to balance the convergence and diversity and improve the predicted accuracy of the Kriging model, respectively; and 4) a novel semisupervised kernel intuitionistic fuzzy cluster validity index is defined to select the optimal solution from the final nondominated solution set. Experimental results on two color image libraries demonstrate that S <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> MKIFC outperforms state-of-the-art methods in segmentation performance and meanwhile possesses a low time cost.